1. Field of the Invention
The present invention relates to image processing. More particularly, the present invention relates to a method and system for accessing a collection of images in a database.
2. Background Information
Automatic image classification has many important applications. Large image databases or collections require good indexing mechanisms so that images can be categorized effectively, browsed efficiently, and retrieved quickly. Conventional systems store and retrieve specific information from a database using, for example, descriptive information regarding the image file, such as file creation date, file name, file extension and the like. This form of image classification is not significantly different from the classification of any other digital information.
By relying on the file information, only cursory information can be obtained about the file and nothing at all specifically related to the image. For example, an image file could have a name that has no relation to the features or content of the image, such as a black and white image could have the file name “color_image”. Other systems provide classification based on the content of the images, such as flowers, dogs, and the like. In practice, this is usually done by keyword annotation, which is a laborious task.
The amount of digital image information available today due to the evolution of the Internet, low-cost devices (e.g., digital video cameras, digital cameras, video capture cards, scanners and the like), and low-cost storage (e.g., hard disks, CDs, and the like) increases the need to classify and retrieve relevant digital image data efficiently. Unlike text-based retrieval, where keywords are successfully used to index into documents, digital image data retrieval has no easily accessed indexing feature.
One approach to navigating through a collection of images for the purpose of image retrieval is disclosed by Yossi, R., “Perceptual Metrics for Image Database Navigation,” PHD Dissertation, Stanford University May 1999, which is incorporated herein by reference in its entirety. The appearance of an image is summarized by distributions of color or texture features, and a metric is defined between any two such distributions. This metric, called the “Earth Mover's Distance” (EMD), represents the least amount of work that is needed to rearrange the images from one distribution to the other. The EMD measures perceptual dissimilarity which is desirable for image retrieval. Multi-Dimensional Scaling (MDS) is employed to embed a group of images as points in a 2- or 3-dimensional (2D or 3D) Euclidean space so that their distances reflect the image dissimilarities. This structure allows the user to better understand the result of a database query and to refine the query. The user can iteratively repeat the process to zoom into the portion of the image space of interest.
It would be desirable to provide a method and system for interactively accessing a large collection of images contained in a database that have been collected over a period of time and that allows for fast browsing and searching of those images.